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* minor * add image * PR feedback * npm run format:fix of course 4_4 * Remove what is not relevant * pr feedback * PR feedback * revert npm run format * Update docs/docs/FAQ.mdx Co-authored-by: Ben McCann <322311+benmccann@users.noreply.github.com> * Update FAQ.mdx --------- Co-authored-by: Alex <alex.tran1502@gmail.com> Co-authored-by: Ben McCann <322311+benmccann@users.noreply.github.com>
1.8 KiB
1.8 KiB
Remote Machine Learning
To alleviate performance issues on low-memory systems like the Raspberry Pi, you may also host Immich's machine-learning container on a more powerful system (e.g. your laptop or desktop computer):
- Set the URL in Machine Learning Settings on the Admin Settings page to point to the designated ML system, e.g.
http://workstation:3003
. - Copy the following
docker-compose.yml
to your ML system. - Start the container by running
docker compose up -d
.
:::info Starting with version v1.93.0 face detection work and face recognize were split. From now on face detection is done in the immich_machine_learning service, but facial recognition is done in the immich_microservices service. :::
:::note The hwaccel.ml.yml file also needs to be in the same folder if trying to use hardware acceleration. :::
version: '3.8'
services:
immich-machine-learning:
container_name: immich_machine_learning
# For hardware acceleration, add one of -[armnn, cuda, openvino] to the image tag.
# Example tag: ${IMMICH_VERSION:-release}-cuda
image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
# extends:
# file: hwaccel.ml.yml
# service: # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference - use the `-wsl` version for WSL2 where applicable
volumes:
- model-cache:/cache
restart: always
ports:
- 3003:3003
volumes:
model-cache:
Please note that version mismatches between both hosts may cause instabilities and bugs, so make sure to always perform updates together.